Mining Big Data Streams: Better Algorithms or Faster Systems?

Mining Big Data Streams: Better Algorithms or Faster Systems?

The rate at which the world produces data is growing steadily, thus creating ever larger streams of continuously evolving data. However, current (de-facto standard) solutions for big data analysis are not designed to mine evolving streams. So, should we find better algorithms to mine data streams, or should we focus on building faster systems?

In this talk, we debunk this false dichotomy between algorithms and systems, and we argue that the data mining and distributed systems community need to work together to bring about the next revolution in data analysis. In doing so, we introduce Apache SAMOA (Scalable Advanced Massive Online Analysis), an open-source platform for mining big data streams (http://samoa.incubator.apache.org). Apache SAMOA provides a collection of distributed streaming algorithms for data mining tasks such as classification, regression, and clustering. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm, S4, and Samza.

As a case study, we present one of SAMOA's main algorithms for classification, the Vertical Hoeffding Tree (VHT). Then, we analyze the algorithm from a distributed systems perspective, highlight the issue of load balancing, and describe a generalizable solution to it. Finally, we conclude by envisioning system-algorithm co-design as a promising direction for the future of big data analytics.